
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "20"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 105 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 101 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	101.621633	103.146917	inf	inf	29.645749	29.512614	105.267013	84.908697	nan	nan	nan	0.000000	0.000000
2459593	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	271.773587	272.082555	inf	inf	70.634463	79.939457	431.883090	468.937085	nan	nan	nan	0.000000	0.000000
2459592	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.673225	-1.070033	3.984386	-0.343239	0.563266	-0.610715	-1.648043	-0.107172	0.0296	0.0313	0.0025	nan	nan
2459591	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.061713	-1.405930	31.128503	4.140748	1.679362	1.703037	1.072512	-0.626066	0.6530	0.6808	0.3827	3.287383	3.514170
2459590	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.311297	-1.333839	28.206414	4.280609	1.745643	1.199080	1.775302	-0.358132	0.6539	0.6787	0.3766	4.161705	4.076900
2459589	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	339.177528	338.562502	inf	inf	57.642361	60.662620	435.888445	445.883224	nan	nan	nan	0.000000	0.000000
2459588	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.308338	-1.149469	30.937940	4.048738	11.960000	1.256456	12.887237	1.683602	0.7678	0.7563	0.2215	10.009845	6.884605
2459587	dish_ok	100.00%	-	-	-	100.00%	0.00%	350.646372	351.180775	inf	inf	103.075332	106.475685	557.408720	587.994017	nan	nan	nan	0.000000	0.000000
2459586	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.120637	-1.425344	2.557487	-0.925552	3.365907	-1.315069	0.829498	-1.033395	0.6637	0.6781	0.3687	3.779596	3.683584
2459585	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	7.910021	-0.481832	4.647195	-0.129337	3.578484	0.098729	0.909389	-0.127528	0.0322	0.0326	0.0014	0.000000	0.000000
2459584	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	18.981690	18.361967	7.263479	2.247061	37.474174	2.377513	-0.342996	-1.000267	0.6196	0.6394	0.3760	7.172328	6.184988
2459583	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459582	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.463055	-1.128151	8.581961	-0.393448	1.224770	0.372272	-0.066501	-0.758463	0.6494	0.6768	0.3962	3.583330	3.687998
2459581	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.741930	-1.570975	10.082093	-0.861546	1.625287	-0.131520	0.301062	-0.740875	0.6406	0.6707	0.4066	3.539018	3.617661
2459580	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.651229	-1.291639	9.829476	-0.675651	0.657518	-0.610696	0.205952	-0.831197	0.6584	0.6839	0.3919	4.133242	4.136876
2459579	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.508059	-1.023957	11.507205	-0.168645	0.884360	-0.401448	0.632611	-0.470771	0.6542	0.6822	0.3944	3.779534	4.135812
2459578	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.446221	-0.394890	1.154078	-0.474815	1.132640	-0.535283	1.385698	-1.005709	0.0302	0.0333	0.0027	nan	nan
2459577	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.939113	-1.374730	9.387690	-0.474174	0.385107	-0.112072	0.406354	-0.612019	0.6591	0.6860	0.3928	3.490985	3.478957
2459576	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.747606	-0.967231	7.315617	-0.004494	2.886796	-0.265542	0.918282	-0.336713	0.6770	0.6994	0.3899	2.550271	2.526087
2459575	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.994662	-0.947342	6.959814	-0.273020	6.323447	-0.830136	11.924013	-0.253192	0.7397	0.7472	0.3404	0.000000	0.000000
2459574	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.381341	-1.174503	6.951005	-0.361670	2.491177	-0.180033	1.539374	-0.640538	0.6805	0.7032	0.3758	3.716378	3.541518
2459573	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459572	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459571	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.022600	-0.396104	1.011229	-0.651846	1.221020	-1.513985	1.225904	-1.014505	0.0312	0.0348	0.0030	nan	nan
2459570	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459569	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.281900	-0.995804	7.901195	-0.495435	11.394903	-0.666884	10.703947	-0.374922	0.7378	0.7484	0.2547	4.747482	4.314198
2459566	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.250767	-1.146659	9.231699	-0.623123	2.935743	0.271842	0.120564	-0.831260	0.6647	0.6866	0.4049	0.000000	0.000000
2459565	dish_ok	0.00%	-	-	-	100.00%	0.00%	1.633766	1.633766	1.509412	1.509412	0.566250	0.566250	0.074197	0.074197	nan	nan	nan	0.000000	0.000000
2459564	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.434380	1.678159	-3.846846	1.302226	-1.337703	-0.903377	-0.554723	-1.599868	0.6944	0.6913	0.3987	2.837237	2.556405
2459563	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.971012	5.437879	9.786916	20.658692	2.297735	11.680060	-0.075806	-1.733389	0.6762	0.6712	0.4052	3.200629	2.818714
2459562	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.800941	0.807264	-2.472093	0.174582	-1.440457	-1.328370	-0.642229	-1.334901	0.6880	0.6864	0.3916	3.207434	2.667125
2459561	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.403469	1.372968	-1.999835	0.733968	-1.322651	-1.227375	-1.073317	-2.220838	0.6907	0.6885	0.3886	3.781991	2.917380
2459560	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.338506	1.040822	-2.366639	0.520813	-1.081245	-0.596591	-0.732925	-1.706947	0.6856	0.6827	0.4016	3.934704	3.213479
2459559	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.502367	1.682689	-1.915335	0.982794	-1.317162	-0.011343	-0.700573	-1.495313	0.6881	0.6912	0.3941	2.728349	2.462688
2459558	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459557	dish_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0361	0.0324	0.0023	nan	nan
2459556	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.036778	6.026869	5.482753	11.037533	3.434891	10.440904	-1.565809	-2.938636	0.6914	0.6909	0.3904	2.741117	2.633678
2459554	dish_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6844	0.6792	0.4118	nan	nan
2459553	dish_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6891	0.6839	0.4029	nan	nan
2459552	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459551	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459550	dish_ok	-	96.71%	96.71%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0446	0.0426	0.0009	nan	nan
2459549	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.674582	5.343586	4.962729	11.360223	1.874952	12.057978	-0.714061	-2.420159	0.6826	0.6778	0.4140	2.588535	2.480684
2459542	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.806602	5.568924	10.260001	22.759768	-0.470099	0.895436	-0.952265	-1.913572	0.7477	0.7318	0.3726	3.187164	2.555617
2459541	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459540	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.669795	2.210505	-0.761784	1.950516	-2.555817	0.367968	-1.135035	-1.061949	0.7449	0.7231	0.3679	3.028547	2.691168
2459536	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.142083	8.461828	9.002280	18.856781	8.363144	18.085235	-0.967124	-3.780016	0.7222	0.7203	0.4226	3.604170	3.381344
2459535	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.673360	1.561617	-1.015350	1.661983	-3.134544	0.662941	-0.673322	-1.502971	0.7774	0.7459	0.3844	2.850243	2.729903
2459534	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.892528	1.165701	-1.611997	0.759581	-2.126004	-0.169234	1.829077	-1.474507	0.7588	0.7481	0.3679	6.615992	6.249973
2459533	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.698707	5.918123	5.218999	11.490249	2.195065	10.880400	-0.973565	-2.199259	0.7120	0.7061	0.4055	2.706298	2.639209
2459532	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.066812	6.949893	7.168083	14.769639	5.717575	13.219432	-0.401987	-2.041763	0.7044	0.7020	0.4036	2.950265	2.911165
2459530	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.404883	1.260329	-1.860383	0.643373	-2.147651	-0.796325	-0.704644	-1.511320	0.7323	0.7232	0.4043	2.637164	2.605068
2459527	dish_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459523	dish_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459522	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459513	dish_ok	100.00%	100.00%	100.00%	0.00%	-	-	1.788134	4.337912	1.115410	6.753593	0.732016	-0.040674	0.801757	-1.058134	0.0353	0.0338	-0.0003	nan	nan
2459508	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.563713	7.246303	4.268601	9.351603	4.513016	8.926593	3.719082	10.647829	0.8884	0.9000	0.2687	0.000000	0.000000
2459505	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.742968	4.326866	6.291653	10.993458	0.510244	4.560925	-0.199953	-0.948895	0.8840	0.8425	0.2242	7.320866	7.810432

Antenna Season Report Notebook¶

Antenna 20 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 20, Node 2:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 20: 2459629

Antenna 20: 2459628

Antenna 20: 2459627

Antenna 20: 2459626

Antenna 20: 2459625

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Antenna 20: 2459609

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Antenna 20: 2459595

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Antenna 20: 2459542

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Antenna 20: 2459540

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶